New from Cambridge University Press!

Sociolinguistics from the Periphery "presents a fascinating book about change: shifting political, economic and cultural conditions; ephemeral, sometimes even seasonal, multilingualism; and altered imaginaries for minority and indigenous languages and their users."

INTRODUCTIONWritten by an advocate of Strong Artificial Intelligencepoint of view, the book has every reason to be considered acontroversial one. Eric Baum proposes a computational modelthat is meant to explain everything (mind, evolution,language etc.). When the ambition is so big it is nowonder that there will be voices rising against it. Myreview will have the following structure. In thisintroduction I will try to put the Baum's work in a globalresearch context for a scientific theory of consciousness.I will present then the main ideas in every chapter and Iwill address what I think are inconsistencies and what areweek points that should be elaborated. Finally I willquestion Baum picture of the mind.

Some years ago philosopher John Searle (1980) distinguishedtwo points of view in Artificial Intelligence (AI):a. The Strong AI (SAI) thesis is that human have cognitivestates by implementing the right kind of computation. Itsfollowers' hope is that computer scientists will find theprogram of ''human mind''.b. The Weak AI thesis is that the only significantcontribution of AI to the understanding of humanpsychology is by building useful tools for testingscientific hypothesis.

For a better understanding of Baum enterprise I will usePenrose's classification of theories about mind andSloman's analyses of the field of SAI. According to Penrose(1990) there are four viewpoints on the nature of mind:

A. All thinking, all sensations, feelings, etc. arethe result of implementing the appropriatecomputation. This is SAI.

B. Consciousness is the result of the physicalactions of the brain. The computation just simulatescognitive states but cannot actually produce them.

C. Consciousness is a characteristic of the brainbut the actions of the brain cannot be simulatedcomputationally.

D. Science will never explain consciousness. Thisis the mystical viewpoint on the nature ofconsciousness.

SAI itself is not compact and easy to classify. Thedifferent positions in the field are marked by differentviews on what can be a theory of implementation (see Sloman1992 for a detailed analysis). Because Baum thinks thatSearle can implement the algorithm which supposedlydescribes the language competence of a Chinese speaker, heis an adept of Sloman's T2 thesis which requires that''computation be causally related to an explicit program'' .By relying on Occam's razor for finding the best theorycompatible with the data, Baum escapes a well-knownobjection to SAI. The objection states that if the behavioralone is sufficient for the possession of cognitive statesthen a program implemented as a giant lookup table, whosebehavior is equivalent with the behavior produced by anintelligent system, will posses mentality.

The main tenet of this book is that mind is an evolvedprogram encoded in a compact code (about 10 Mega of DNA).However this code cannot be replicated yet because we havenot the computational resources the evolution had. But withthe aid of evolutionary programming and using Occam's razorwe will succeed in the future to find the program of mind.

SUMMARY AND COMMENTSChapter 1. IntroductionThe introductory chapter has two parts. In the first partthe author lays out in short all his chief ideas. Thesecond part is just a road map to the rest of the book.Baum starts by stating that his purpose is to present apicture of the mind which is consistent with all ourpresent knowledge. He continues by rejecting the mysticalconception of the mind and then he presents SAI as if itwere the only alternative to mysticism. As we saw this isnot true, there are two other theories labeled B and Cabove, which are worth considering. It is necessary atleast to present the other scientifically compatible pointsof view and try to refute them.

Then he presents the main ideas that he tries to sustainand defend in the rest of the book:

1. Mind is a computer program. More precisely mind is amodular program with dedicated subroutines. Thesesubroutines are used in different contexts, thusfacilitating learning. The analogy and the metaphor areexplainable by code reuse.

2. ''Thought is all about semantics.'' For an entity to havemeaning is to have the capacity to capture and exploit ''thecompact structure of the world''.

3. The best theory is the simplest one. If there are manytheories (in our case programs) which explain the samefacts and made accurate predications, to choose the rightone we should use Occam's razor.

Unfortunately this chapter contains some inconsistencies:For example, at page 3 Baum says that: ''The execution of acomputer program is always equivalent to pure syntax.'' Onany construal this statement is false. If a program inexecution (a process) is just syntax and the mind is acomputer program how can mind have semantics? A follower ofSAI would say that a process, if it were to be consideredintelligent, would have semantics.

At the same page the author claims that ''mind typicallyproduces a computer program capable of behaving'', whichimmediately raises the question:Is the mind a computer program or does the mind produces aprogram?

Chapter 2. The Mind Is a Computer Program.The chapter builds in his most part on historicalconsiderations. The author introduces important conceptsfor understanding the computational picture of the mindsuch as Turing machine, universal Turing machine and theself-reproducing automaton of John von Neumann.

Firstly Baum stresses that the creation of the Turingmachine was the consequence of the attempt to answer one ofthe problems put by Hilbert: is there an effectiveprocedure for solving all the problems of mathematics (awell define class of problems as Diophantian equations, tobe more precise)? The problem was independently andnegatively solved by Turing who developed the concept ofTuring machines and by Church who developed the lambdacalculus. Turing machines, lambda calculus and Emil Postproduction systems formalize what an algorithm is. But atthis point someone might ask: what is the link between aTuring machine and the thinking process? The only answerthat Baum gives is that a mathematician mind solving acertain problem is equivalent to the lookup table of theTuring machine and so the states of the mind of themathematicians are computational states. But there are atleast two questions that should be answered:

1. Mathematical thinking is just a small part of whatcan be titled as thinking. Even admitting that the conceptof computation captures the mathematical thinking how aboutthe rest of thinking?

2. Is mathematical thinking computational? Many authors,notably Lucas and Penrose, believe it is not. They use intheir support the celebrated Kurt Godel's theorem, alsomentioned by Baum in this chapter.

Moreover, the account Baum gives to the Hilbert problem isa little bit misleading. He says at page 50 that Godelanswered the problem ''(mentioned earlier in this chapter)in the negative: there is not effective procedure that canprove all the true theorems of mathematics''. Instead ofbeing concerned with this problem, Godel was concerned withother problem posed by Hilbert, namely to give an absoluteconsistency proof and also completeness proof for themathematics. Godel showed that a system equivalent to theRussell-Whitehead Principia Mathematica containsundecidable propositions and one of them is the consistencyof the system.

In the second part of chapter 2 the author describes ''thecomputational process that is life'', a process that createsand maintains us. He gives us some background frombiochemistry and argues that the program of life isisomorphic with a Post Production system.

Chapter 3. The Turing Test, the Chinese Room, and What Computers Can't DoThis chapter is Baum's first attempt to answer some criticsof SAI point of view. He is addressing in principal theproblems of qualia and understanding. The first problemthat Baum takes on is the important problem of experienceor qualia. How is that we can feel, smell and so on if ourminds are just computer programs? Unfortunately Baum'sanswer will be accepted as a valid one only by SAI adepts.Basically, he says that the fact that we cannot accept thatcomputers can have qualia is just a failure of ourimagination. Moreover, he claims that we will accept thiswhen we have a chat with a computer that will insist thatit can have experiences (p. 67).

Baum then skips to the other problem, the problem ofunderstanding. In this context he presents the Turing testand what is considered by most people to be the mostpowerful argument against SAI, Searle's Chinese roomargument (CRA). The way Baum tries to refute the CRA is notnew and it is just a variant of the ''System Reply''.Searle's answer to the ''System Reply'' is that he caninternalize all the elements of the system, he can answerany question an external observer asks, but he will failagain to understand Chinese. Baum rejects Searle'sconclusion that he doesn't understand Chinese and sustainsthat, by internalizing a Turing machine that can answerChinese questions, Searle does indeed understand Chinese.But that is to misunderstand Searle's proof. The thoughtexperiment that Searle proposes has as its point to showthat someone can pass a Turing test without understandinganything, that human understanding is different fromcomputer understanding in that we have intentionality,which computers lack. Baum puzzles us further when (pp. 78)he misidentifies intention with the intentionality: ''Thisconcern that intention is something that only human beingscan have is still reverberating in philosophicalliterature.''

Chapter 4. Occam's Razor and UnderstandingThe theme of this chapter is how symbols in computerprograms can mean something.

Refuting the CRA and accepting the Turing Test as ameasurement for understanding, it is clear that the answerto the problem of how the symbols of a computer can havemeaning will be based on the capacity of programs toexplain a series of facts and make predication. With thismeaning of ''meaning'' in mind Baum shows us how the externaldata can be fit by a good theory. The chapter is a plea forOccam's razor, which is seen by the author to be at theheart of science.

The author presents three formalizations of Occam's razor.The first formalization uses Vapnik-Chervonenkis dimension,the second is based on the description length principle,and the third one is ground on Bayesian probability.

Baum says very few words about neural nets, and this isperhaps the greatest drawback of the book. In addition, thepresentation of neural nets misguides the uniformed readerin that it can create the false impression that neural netsare ''complicated models of brain circuits''. In fact, braincircuits without being their models only inspire neuralnets. The author will want perhaps to discuss this and tocorrect this drawback in a next edition of the book.

Chapter 5. OptimizationIf the chapter before was concerned with the relationbetween compact programs and the data they are thedescription of, this chapter deals with the heuristic offinding the best program consistent with some given data.The author rules out the prospect of searching through allthe possibilities and then finding the most compactdescription consistent with the data due to the fact thatsuch an algorithm has an exponential complexity. He alsostresses that we don't need the smaller possiblerepresentation in order to extract semantics, but onesufficiently smaller than the data. Baum argues that thesolution to this problem could be a general optimizationtechnique known as hill climbing. The hill climbing and itsadvantages are nicely exemplified with the TravelingSalesman Problem. The author speculates that a similartechnique was used by evolution in its searching formeaningful possibilities.

Chapter 6. Remarks on Occam's RazorBaum starts the most substantive part of the chapter 6 witha discussion of a critique of neural networks. The critiqueis that someone who looks at the inner structure of theneural networks cannot understand what the net is doing.Then the author elaborates this point of view, but notsufficiently because some things remain unclear. BecauseBaum doesn't make explicit his point of view in the so-called ''Systematicity debate'' launched in 1988 by Fodorand Pylyshyn (1988) I cannot tell what Baum thinks aboutneural networks. However, my opinion is that Baum thinksthat connectionism can account for higher cognitivefunctions only but implementing the classical model. Baumconfuses me further when he writes:'' Neural nets are not sufficiently powerful to describeminds. One must talk instead about more powerfulprogramming languages.'' (pp 133)

Does Baum think that neural networks are programming languages!? If not, what else does this mean?

Then the author compares DNA with the source code and themind with the executable. Baum extends the analogy bycomparing commentaries in programming languages with thebase pairs in the DNA which are not read duringdevelopment. The chapter ends with a discussion ofgeneralization in neural networks and with a sketch of theproof of the lower bound theorem.

Chapter 7. Reinforcement LearningBaum argues that a passive framework, which has as a goaljust the data classification, is not sufficient to accountfor consciousness. Instead we need a more powerful modelwhich can account for the interaction between robots (us)and the world. The author discusses some reinforcementlearning techniques and argues that neural nets are tooweak representations.

Chapter 8. Exploiting StructureThe author draws the difference between recognizingstructure and exploiting structure. He correctly arguesthat a theory that only accounts for data classification isfundamentally incomplete. Three problems which involvestructure exploitation are presented (Blocks World problem,the game of Chess and Go). Baum shows the insufficienciesof already tried computational approaches for solving theseproblems. He sustains that evolution has ''trained'' the mindon vast number of problems. Now the mind has the capacityto generalize and to solve problems it was not trained forsuch as the above-mentioned games. In these cases theprogram of mind is better because he has the capacity to:''analyze new problems such as chess into a collection oflocalized objects that interact causally ... ''(pp.196).Unfortunately the author does not elaborate this point andI cannot understand what notion of causality he has inmind. Moreover, he should further explain why this capacitycannot be captured by present computer programs.

Chapter 9. Modules and MetaphorsThe chapter is a plea for the modular structure of the mindand for the metaphorical nature of thought. In support ofthe former the author brings evidence from cognitivescience biology and psychology. After succinctly presentingthe ideas of Lakoff and Johnson he explains the metaphoricnature of thinking by code reuse.

Chapter 10. Evolutionary ProgrammingSome computational experiments for evolving code arediscussed. The author together with his colleague tries tosolve Blocks World problem by using evolutionaryprogramming. They succeeded to evolve a program thatimplements the same algorithm that we use when trying tosolve this problem. However the resulted code is notsuperior to a code not written by this method.

Chapter 11. IntractabilityA more complete and detailed analysis of the techniquesused by computer scientists to solve general classes ofproblems is given, along with examples of polynomial timemapping of instances of NP complete problems. There arealso presented some experiments with evolutionary roboticsand it is argued that constraints propagation allows forsolutions for ''intractable problems''.

Chapter 12. The Evolution of LearningA parallel between learning and development is drawn. It ispostulated that the learning process is largely dependenton the inductive biases that evolution produced. Moreover,learning affects evolution by ''Baldwin effect'' (the abilityof individuals to learn can guide the evolutionary process)and culture. However, for Baum, the concept ''culture'' hasnot its usual meaning, but it means passing the informationthrough ''parental instruction''. Perhaps for Baum culturemeans the set of acquired behaviors? An example of culturalinteraction would be the parental instructions given bymother bear to her children (p. 335). The author believesthat most of our concepts are innate. Baum is a supporterof Chomsky's ideas and he states that from the perspectiveof evolutionary programming: ''Chomsky's proposal that thereis some universal grammar wired into genome istautological'' (pp. 343).

Chapter 13. Language and Evolution of ThoughtLanguage has a purely communication function. Language iswhat differentiates animals and humans. Baum proposes atheory of language where language words are seen as labelsfor computational modules. Grammar, which has a nonstandarddefinition here, is seen as a mapping function fromcombinations of words in a sentence to corresponding code.It is argued that thought has nothing to do with languagebecause concepts predate language. Learning new wordsinvolve attaching labels to already existing computationalmodules.

Chapter 14. The Evolution of ConsciousnessThis chapter deals primarily with consciousness, awareness,qualia and free will. In the author's view, evolutionproduced mind and finally consciousness. Baum tries to givean account for the notion of self. Even if he thinks thatthe mind is a distributed program with many subroutines,the fact that the subroutines are working toward the sameend build the self. The author stresses also that we arenot aware of most of our computation and that our awarenessis just a module of mind which concentrates the results ofvast amounts of unconsciousness computation. One goodargument against physicalism is that it cannot account forsensations and feelings. Baum argues against this on theground that sensations are necessary for the evolutionprocess and are built into our program at a ''fundamentallevel''. But this doesn't address the question in that hepresupposes the existence of sensations, and does not showhow the execution of a computer code gives rise tosensations. The author thinks that free will is entirelyconsistent with physicalism. He says that free will doesnot exist but it is a useful concept in predicting thebehavior of others.

FINAL REMARKSBaum's book raises many questions. His simple system ofexplaining everything could be extensively questioned. Inwhat follows I will raise some possible objections.However, not being competent in genetics, I will notquestion the validity of Baum's hypotheses from this pointof view.

1. Baum states that he offers a theory of mind compatiblewith our present knowledge. But it can turn out that ourcurrent knowledge is not sufficient for explaining mind.For instance, a theory of physics compatible with the 17-century knowledge would be plainly false.

2. Baum does not give a theory of implementation. Thismakes his construction vulnerable at Putnam's (1988) orSearle's (1990) arguments. Putnam for example argued thatif we are allowed to consider arbitrary disjunction ofphysical states as realization of the formal states of anautomaton then the result we will obtain is that any openphysical system implements any finite state automaton(FSA). Similarly, Searle argues that because syntax is anobserver relative notion, then a wall or stomach can beseen as implementing any computation.

3. Surprisingly for a theory that tries to explain thought,Baum doesn't refer to propositional attitudes and doesn'ttry a naturalistic reduction of Intentionality. Withoutthis his model has not force and cannot explain anything.

4. He claims (chapter 8) that he solved the oldphilosophical problem of whether the world exists or isjust an illusion but he is just begging the question. Hepresupposes the existence of physical objects and of acode as the compact representation of the world and thentries to prove that the world really exists.

5.His theory of language is roughly this:a. Every concept corresponds to a piece of code (asubroutine).b. The most part of concepts is innate.c. The meaning of expressions is compositional and it isobtained by a module calling other modules.d. Learning new words means to attach labels to presentlyexisting modules.

This raises at least the following questions: If themost part of concepts is innate how can they fit in 10MG of code in DNA? How can a speaker choose the rightsense of a word when the word is ambiguous (In Baum'sformulation, how a subroutine knows what subroutine tocall)? How does this model treat pragmatics? How is thenew code added and how is it compiled when we learn newconcepts?

6.Baum seems to think that evolution itself is acomputational process (note that this is different fromsaying that the evolution can be simulatedcomputationally). What algorithm does evolution implement?For example Baum says: ''evolution effectively searchedover combinations of meaningful macros. Add long legs, andsee if that helps''. If evolution tried many possibilitieswhere is the evidence?

Despite these problems, Baum's book was a pleasure to read. The author manages to explain hard concepts like self-reproducing automata and NP completeness to those lessfamiliar with them. Moreover, he has a keen sense ofhumor that adds joy to the reading.

ABOUT THE REVIEWER:
ABOUT THE REVIEWEREduard Barbu is a researcher at the Romanian Institute forArtificial Intelligence. He was involved in severalEuropean projects. His interests are: formal and lexicalontology, cognitive science, philosophy of language andmind. He is presently working at a dictionary of Philosophyof Mind.